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In laser based powder-bed fusion of metals (PBF-LB/M), parts are fabricated by melting layers of powder using a high-intensity laser beam. During this process, the material is exposed to rapid cooling rates and intense thermal gradients, which are the underlying causes of residual stress formation and development of a unique microstructure in these components. Therefore, understanding the heat transfer phenomenon and reliably representing exposed temperature profiles in simulation frameworks are prerequisites for studying the microstructure and residual stress development during the PBF-LB/M process. This work employs a combination of experimental measurements and model development to study this phenomenon. Thermal properties of Hastelloy X were measured in the as-deposited state and used to setup finite element (FE) thermal simulations of the PBF-LB/M process. In addition, in-situ temperature evolutions near the laser tracks were measured by instrumenting thin-wall structures with K-type thermocouples in a two-stage fabrication process. The gathered data was used to calibrate uncertain modelling parameters, and ultimately, the simulation framework could closely represent the measured temperature histories. To address the high computational cost of FE thermal simulations, an adaptive-local/global multiscale modelling approach was proposed, which substantially reduced computation times without compromising the accuracy of the results. The modelling files and scripts are available in github.

Transdermal drug delivery is a rapidly expanding industry with significant advantages over conventional oral and intravenous methods. However, in order to gain a better understanding of drug permeation mechanisms and obtain accurate estimates of drug permeability to the skin, there is an urgent need for modeling techniques. In this thesis, we present a multi-scale modeling approach to investigate solute transport through the human stratum corneum. Firstly, continuum finite element brick-and-mortar models were constructed to study water permeation. Subsequently, molecular dynamics simulations were employed to investigate the permeation of various solutes through lipid bilayers within the stratum corneum. [...], Thesis (PhD Doctorate), Doctor of Philosophy (PhD), School of Environment and Sc, Science, Environment, Engineering and Technology, Full Text

Microkinetic modelling facilitates a detailed description of chemical reactions and has been an important tool in heterogeneous catalysis research to resolve mechanisms at the molecular level. For industrial processes however, transport phenomena inside the chemical reactor can strongly influence performance. In order to understand the interplay between kinetics and transport properties, embedding of microkinetics inside reactor models is necessary yet inhibited by the computational cost of microkinetic simulations. To relieve this issue, we have developed artificial neural networks to substitute microkinetic models and improve computational performance. These networks are capable of accurately reproducing reaction rates across different regimes while obeying chemical conservation laws. A workflow is detailed here for the automated construction of the networks and the cost-efficient generation of the required training datasets using adaptive mesh refinement. These techniques allowed for construction of a reactor model for Fischer–Tropsch synthesis with full mechanistic detail. This model elucidated the undesired promotion of CO2 formation at higher conversions following re-adsorption of in-situ generated water, inhibition of this effect through formation of a carbon reservoir and lowered catalyst activity due to mass transfer limitations.

Neural closure models have recently been proposed as a method for efficiently approximating small scales in multiscale systems with neural networks. The choice of loss function and associated training procedure has a large effect on the accuracy and stability of the resulting neural closure model. In this work, we systematically compare three distinct procedures: “derivative fitting”, “trajectory fitting” with discretise-then-optimise, and “trajectory fitting” with optimise-then-discretise. Derivative fitting is conceptually the simplest and computationally the most efficient approach and is found to perform reasonably well on one of the test problems (Kuramoto-Sivashinsky) but poorly on the other (Burgers). Trajectory fitting is computationally more expensive but is more robust and is therefore the preferred approach. Of the two trajectory fitting procedures, the discretise-then-optimise approach produces more accurate models than the optimise-then-discretise approach. While the optimise-then-discretise approach can still produce accurate models, care must be taken in choosing the length of the trajectories used for training, in order to train the models on long-term behaviour while still producing reasonably accurate gradients during training. Two existing theorems are interpreted in a novel way that gives insight into the long-term accuracy of a neural closure model based on how accurate it is in the short term.

Neural closure models have recently been proposed as a method for efficiently approximating small scales in multiscale systems with neural networks. The choice of loss function and associated training procedure has a large effect on the accuracy and stability of the resulting neural closure model. In this work, we systematically compare three distinct procedures: “derivative fitting”, “trajectory fitting” with discretise-then-optimise, and “trajectory fitting” with optimise-then-discretise. Derivative fitting is conceptually the simplest and computationally the most efficient approach and is found to perform reasonably well on one of the test problems (Kuramoto-Sivashinsky) but poorly on the other (Burgers). Trajectory fitting is computationally more expensive but is more robust and is therefore the preferred approach. Of the two trajectory fitting procedures, the discretise-then-optimise approach produces more accurate models than the optimise-then-discretise approach. While the optimise-then-discretise approach can still produce accurate models, care must be taken in choosing the length of the trajectories used for training, in order to train the models on long-term behaviour while still producing reasonably accurate gradients during training. Two existing theorems are interpreted in a novel way that gives insight into the long-term accuracy of a neural closure model based on how accurate it is in the short term.

A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains.

The developments of thermodynamics, transport processes, chemical kinetics, electrochemistry, membrane sciences, physical and organic chemistry are used to derive elementary components of flow battery models. For example, studying chemical kinetics of a given redox pair results in a formulation of auxiliary equations and all the necessary parameters to describe the current density output given the applied potential and species concentrations. Thermodynamics usually answers questions on cell state at rest (equilibrium) and becomes useful in prediction of e.g. open-circuit potentials or chemical equilibria in the electrolyte. The framework of non-equilibrium thermodynamics is useful in deriving rudimentary relationships to describe coupled transport phenomena, especially in concentrated electrolytes. Elementary models are usually employed to either extract pertinent parameters for FB cell operation (kinetic constants, standard potentials, catalyst ageing) or to study degradation processes occurring in porous electrodes on smaller scales (micrometers).

Osteoarthritis (OA) has a complex, heterogeneous and only partly understood etiology. There is a definite role of joint cartilage pathomechanics in originating and progressing of the disease. Although it is still not identified precisely enough to design or select targeted treatments, the progress of this year's research demonstrates that this goal became much closer. On multiple scales - tissue, joint and whole body - an increasing number of studies were done, with impressive results. (1) Technology based instrument innovations, especially when combined with machine learning models, have broadened the applicability of biomechanics. (2) Combinations with imaging make biomechanics much more precise & personalized. (3) The combination of Musculoskeletal & Finite Element Models yield valid personalized cartilage loads. (4) Mechanical outcomes are becoming increasingly meaningful to inform and evaluate treatments, including predictive power from biomechanical models. Since most recent advancements in the field of biomechanics in OA are at the level of a proof op principle, future research should not only continue on this successful path of innovation, but also aim to develop clinical workflows that would facilitate including precision biomechanics in large scale studies. Eventually this will yield clinical tools for decision making and a rationale for new therapies in OA., Biomechatronics & Human-Machine Control

Service lifetimes of polymers and polymer composites are impacted by environmental ageing. The validation of new composites and their environmental durability involves costly testing programs, thus calling for more affordable and safe alternatives, and modelling is seen as such an alternative. The state-of-the-art models are systematized in this work. The review offers a comprehensive overview of the modular and multiscale modelling approaches. These approaches provide means to predict the environmental ageing and degradation of polymers and polymer composites. Furthermore, the systematization of methods and models presented herein leads to a deeper and reliable understanding of the physical and chemical principles of environmental ageing. As a result, it provides better confidence in the modelling methods for predicting the environmental durability of polymeric materials and fibre-reinforced composites., Applied Mechanics

We studied the formation of AuRh nanoalloys (between 20-150 atoms) in the gas phase by means of Molecular Dynamics (MD) calculations, exploring three possible formation processes: one-by-one growth, coalescence, and nanodroplets annealing. As a general trend, we recover a predominance of Rh@Au core-shell ordering over other chemical configurations. We identify new structural motifs with enhanced thermal stabilities. The physical features of those selected systems were studied at the Density Functional Theory (DFT) level, revealing profound correlations between the nanoalloys morphology and properties. Surprisingly, the arrangement of the inner Rh core seems to play a dominant role on nanoclusters' physical features like the HOMO-LUMO gap and magnetic moment. Strong charge separations are recovered within the nanoalloys suggesting the existence of charge-transfer transitions.

Hydrides based on magnesium and intermetallic compounds provide a viable solution to the challenge of energy storage from renewable sources, thanks to their ability to absorb and desorb hydrogen in a reversible way with a proper tuning of pressure and temperature conditions. Therefore, they are expected to play an important role in the clean energy transition and in the deployment of hydrogen as an efficient energy vector. This review, by experts of Task 40 'Energy Storage and Conversion based on Hydrogen' of the Hydrogen Technology Collaboration Programme of the International Energy Agency, reports on the latest activities of the working group 'Magnesium- and Intermetallic alloys-based Hydrides for Energy Storage'. The following topics are covered by the review: multiscale modelling of hydrides and hydrogen sorption mechanisms; synthesis and processing techniques; catalysts for hydrogen sorption in Mg; Mg-based nanostructures and new compounds; hydrides based on intermetallic TiFe alloys, high entropy alloys, Laves phases, and Pd-containing alloys. Finally, an outlook is presented on current worldwide investments and future research directions for hydrogen-based energy storage.

[EN] Cardiovascular diseases currently have a major social and economic impact, constituting one of the leading causes of mortality and morbidity. Personalized computational models of the heart are demonstrating their usefulness both to help understand the mechanisms underlying cardiac disease, and to optimize their treatment and predict the patient's response. Within this framework, the Spanish Research Network for Cardiac Computational Modelling (VHeart-SN) has been launched. The general objective of the VHeart-SN network is the development of an integrated, modular and multiscale multiphysical computational model of the heart. This general objective is addressed through the following specific objectives: a) to integrate the different numerical methods and models taking into account the specificity of patients; b) to assist in advancing knowledge of the mechanisms associated with cardiac and vascular diseases; and c) to support the application of different personalized therapies. This article presents the current state of cardiac computational modelling and different scientific works conducted by the members of the network to gain greater understanding of the characteristics and usefulness of these models., [ES] Las enfermedades cardiovasculares tienen en la actualidad un gran impacto social y económico y constituyen una de las principales causas de mortalidad y morbilidad. Los modelos computacionales personalizados del corazón están demostrando ser útiles tanto para ayudar a comprender los mecanismos subyacentes a las patologías cardiacas como para optimizar su tratamiento prediciendo la respuesta del paciente. En este contexto, se ha puesto en marcha la Red Española de Investigación en Modelización Computacional Cardiaca (V-Heart SN). El objetivo general de V-Heart SN es el desarrollo de un modelo computacional multifísico y multiescala integrado del corazón. Este objetivo general se aborda a través de los siguientes objetivos específicos: a) integrar los diferentes modelos numéricos teniendo en cuenta la especificidad de los pacientes; b) ayudar a avanzar en el conocimiento de los mecanismos asociados a las diferentes patologías cardiacas y vasculares; y c) apoyar la aplicación de terapias personalizadas. Este artículo presenta el estado actual de la modelización computacional cardiaca y los diferentes trabajos científicos desarrollados por los miembros de la red para favorecer una mayor comprensión de las características y utilidad de los modelos.

This thesis developed a multiscale modelling method for simulating the airflow behavior and particle motion in the human airways. Its purpose was to increase the simulation efficiency without scarifying much accuracy by incorporating three different multiscale techniques. The multiscale method was implemented and verified by various benchmark studies. The multiscale method has been proved feasible to speed up the human airway simulations with acceptable accuracy. Therefore, the developed multiscale modelling method opens a new avenue to clinical applications.

This thesis was an investigation into the multiscale nature of moisture movement within food material during drying. It examines the moisture composition within cells and pores and how it evolves with time to develop an accurate and generalised property approach for moisture diffusivity. In the future, the work will aid in the development of optimum drying systems with improved food quality.

To reduce the CO2 footprint of construction materials, concrete producers blend their cement with Supplementary Cementitious Materials (SCMs). And even though such blended systems are eco-friendlier than the Ordinary Portland Cement (OPC), they are required to respect standards. In this context, understanding and predicting the durability of blended concrete is important for optimizing the design of new cementitious materials. Even though chloride ingress is one of the most common problems for reinforced concrete, the quantification of the link between ionic transport and the microstructure is still a challenge. Modelling transport in cementitious materials by Fickian processes usually fails to predict experimental results and particularly those of blended systems. Chloride ingress is thought to be influenced, at the nanoscale, by the adsorption of ions on the hydrates surface due to the formation of an Electrical Double Layer (EDL). Therefore, a multiscale approach will be adapted to model the phenomena arising at different scales. The method for the calculation of ionic diffusivities will couple the Metropolis Monte Carlo algorithm (MC) to the Poisson Nernst-Planck (PNP) equation. First, the Monte Carlo is applied to the Grand Canonical ensemble to determine the ions distributions in the pores as a function of the pore radius. Once the densities of all ionic species are determined, the PNP system is resolved with the Finite Element Method (FEM) and effective diffusion coefficients are calculated as a function of the pore size. The contribution of the microstructure on the transport is to be investigated by developing a consistent microstructural model of the C-S-H, the main hydration product.

The M4F project brings together the fusion and fission materials communities working on the prediction of radiation damage production and evolution and its effects on the mechanical behaviour of irradiated ferritic/martensitic (F/M) steels. It is a multidisciplinary project in which several different experimental and computational materials science tools are integrated to understand and model the complex phenomena associated with the formation and evolution of irradiation induced defects and their effects on the macroscopic behaviour of the target materials. In particular the project focuses on two specific aspects: (1) To develop physical understanding and predictive models of the origin and consequences of localised deformation under irradiation in F/M steels; (2) To develop good practices and possibly advance towards the definition of protocols for the use of ion irradiation as a tool to evaluate radiation effects on materials. Nineteen modelling codes across different scales are being used and developed and an experimental validation programme based on the examination of materials irradiated with neutrons and ions is being carried out. The project enters now its 4th year and is close to delivering high-quality results. This paper overviews the work performed so far within the project, highlighting its impact for fission and fusion materials science.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement no. 875489 (SONAR), Redox flow batteries are an emerging technology for grid energy storage applications thanks to their promising properties, such as long cycle life and safety. Porous electrodes are a core component of flow batteries that facilitate the electron transfer between the liquid electrolyte and solid electrode by providing high specific surface areas. We are interested in macroscopic homogenized descriptions of the coupled processes of mass transport and heterogeneous reactions in porous electrodes, allowing for efficient simulations over macroscopic domains. The effective macroscopic properties, such as the dispersion tensor or the effective reaction rate depend on the pore-scale properties of the porous electrode, such as the morphology and surface properties of the electrode. Here we consider periodic porous media with simplified geometries. The electrolyte is modelled as a dilute, multicomponent mixture occupying the pore-space. We use the method of volume averaging for upscaling the pore-scale problem to obtain effective macroscopic descriptions and study their dependence on the pore-scale properties. In future work, we intend to consider more complex electrochemical interface descriptions based on the framework of non-equilibrium thermodynamics, which allow incorporating additional interface properties that could be provided by lower-scale descriptions, such as kinetic Monte Carlo simulations.

Prediction of material performance in fusion reactor environments relies on computational modelling, and will continue to do so until the first generation of fusion power plants come on line and allow long-term behaviour to be observed. In the meantime, the modelling is supported by experiments that attempt to replicate some aspects of the eventual operational conditions. In 2019, a group of leading experts met under the umbrella of the IEA to discuss the current position and ongoing challenges in modelling of fusion materials and how advanced experimental characterisation is aiding model improvement. This review draws from the discussions held during that workshop. Topics covering modelling of irradiation-induced defect production and fundamental properties, gas behaviour, clustering and segregation, defect evolution and interactions are discussed, as well as new and novel multiscale simulation approaches, and the latest efforts to link modelling to experiments through advanced observation and characterisation techniques.

Ultra-High Strength Concrete (UHSC) is now being widely adopted in the construction industry due to the very high compressive strength, high elastic modulus, excellent durability characteristics etc. In the macroscopic scale, UHSC is considered as a homogenous and isotropic material when designing concrete structures. However, in reality UHSC is a highly heterogeneous material consisting of different length scales of heterogeneities known as multiscales of concrete. These multiscales of concrete can be identified as macroscale, mesoscale, microscale, nanoscale, and atomic scale. Mechanical and durability characteristics and the nonlinear behaviour of UHSC in the macroscale depend on the characteristics of the constituents in these multiscales. Modelling these individual scales and developing a connectivity between the scales are essential in exploring how the phases in each scale are contributing to the macroscopic behaviour of UHSC. Even though previous researchers have focused on individual scales of UHSC, a comprehensive study examining all the multiscales present in UHSC and developing a framework to link these scales to explore the contribution from each scale to the macroscopic behaviour is lacking. This research tries to address the question of how to model UHSC in different scales and how the intrinsic material properties of constituent phases in the different spatial scales contribute to the overall macroscopic behaviour. The main aim of this investigation is to create a comprehensive multiscale modelling framework to model UHSC in various length scales, and to bridge the gap between these scales and upscale and predict the macroscopic behaviour of UHSC using constituent properties at nano, micro and meso scales. In this research, mesoscale, microscale, and nanoscale of concrete are modelled using advanced modelling techniques such as finite element representative element modelling (FE-RVE) and finite element mesoscale modelling. An experimental programme i

Prediction of material performance in fusion reactor environments relies on computational modelling, and will continue to do so until the first generation of fusion power plants come on line and allow long-term behaviour to be observed. In the meantime, the modelling is supported by experiments that attempt to replicate some aspects of the eventual operational conditions. In 2019, a group of leading experts met under the umbrella of the IEA to discuss the current position and ongoing challenges in modelling of fusion materials and how advanced experimental characterisation is aiding model improvement. This review draws from the discussions held during that workshop. Topics covering modelling of irradiation-induced defect production and fundamental properties, gas behaviour, clustering and segregation, defect evolution and interactions are discussed, as well as new and novel multiscale simulation approaches, and the latest effort s to link modelling to experiments through advanced observation and characterisation techniques., QC 20210819

Cancer is a complex illness that affects millions of people every year. Amongst the most frequently encountered variants of this illness are solid tumours. The growth of solid tumours depends on a large number of factors such as oxygen concentration, cell reproduction, cell movement, cell death, and vascular environment. The aim of this thesis is to provide further insight in the interconnections between these factors by means of numerical simulations. We present a multiscale model for tumor growth by coupling a microscopic, agent-based model for normal and tumor cells with macroscopic mean-field models for oxygen and extracellular concentrations. We assume the cell movement to be dominated by Brownian motion. The temporal and spatial evolution of the oxygen concentration is governed by a reaction-diffusion equation that mimics a balance law.To complement this macroscopic oxygen evolution with microscopic information, we propose a lattice-free approach that extends the vascular distribution of oxygen. We employ a Markov chain to estimate the sprout probability of new vessels. The extension of the new vessels is modeled by enhancing the agent-based cell model with chemotactic sensitivity. Our results include finite-volume discretizations of the resulting partial differential equations and suitable approaches to approximate the stochastic differential equations governing the agent-based motion. We provide a simulation framework that evaluates the effect of the various parameters on, for instance, the spread of oxygen. We also show results of numerical experiments where we allow new vessels to sprout, i.e. we explore angiogenesis. In the case of a static vasculature, we simulate the full multiscale model using a coupled stochastic/deterministic discretization approach which is able to reduce variance at least for a chosen computable indicator, leading to improved efficiency and a potential increased reliability on models of this type.

Graphene and borophene are highly attractive two-dimensional materials with outstanding physical properties. In this study we employed combined atomistic continuum multi-scale modeling to explore the effective thermal conductivity of polymer nanocomposites made of polydimethylsilox-ane (PDMS) polymer as the matrix and graphene and borophene as nanofillers. PDMS is a versatile polymer due to its chemical inertia, flexibility and a wide range of properties that can be tuned during synthesis. We first conducted classical Molecular Dynamics (MD) simulations to calculate the thermal conductance at the interfaces between graphene and PDMS and between borophene and PDMS. Acquired results confirm that the interfacial thermal conductance between nanosheets and polymer increases from the single-layer to multilayered nanosheets and finally converges, in the case of graphene, to about 30 MWm−2 K−1 and, for borophene, up to 33 MWm−2 K−1. The data provided by the atomistic simulations were then used in the Finite Element Method (FEM) simulations to evaluate the effective thermal conductivity of polymer nanocomposites at the continuum level. We explored the effects of nanofiller type, volume content, geometry aspect ratio and thickness on the nanocomposite effective thermal conductivity. As a very interesting finding, we found that borophene nanosheets, despite having almost two orders of magnitude lower thermal conductivity than graphene, can yield very close enhancement in the effective thermal conductivity in comparison with graphene, particularly for low volume content and small aspect ratios and thicknesses. We conclude that, for the polymer-based nanocomposites, significant improvement in the thermal conductivity can be reached by improving the bonding between the fillers and polymer, or in other words, by enhancing the thermal conductance at the interface. By taking into account the high electrical conductivity of borophene, our results suggest borophene nanosheets as promi

The macroscopic behavior of complex heterogeneous materials is governed by the interactions between their constituents at the microstructural scale. This has fostered a large number of contributions on multiscale modelling of materials, with the aim of combining the behaviors of their constituents with their geometrical organization. However, many contributions focused on the investigation of advanced physics while using simplified geometries to represent the spatial organization of the phases. Conversely, the recent development of experimental imaging techniques has allowed the use of real geometries in full microstructural simulations. This exploitation of experimental images however convolutes the effects of all morphological features, and does not allow unravelling the dominant geometrical effects. Recent developments allow bridging this gap by using advanced geometry representation tools. This chapter presents an integrated set of tools to address complex geometries based on distance fields and level sets. This integrated approach allows (i) generating microstructural geometries in a controlled fashion for a wide range of microstructural morphologies starting from a population of inclusions, and (ii) discretizing the obtained geometries using high quality conformal tetrahedral finite element meshes. Based on a presentation of explicit and implicit descriptions of geometries, the use of distance fields to improve the efficiency of packing algorithms is presented. The recombination of such distance fields to generate Representative Volume Elements (RVEs) for various microstructural morphologies is next detailed. Inclusion-based materials and coated and cemented materials are considered. Distance field-based morphing of inclusions is also used to obtain generalized spatial tessellations. Further generalizations useful for other types of materials such as foams and composites are briefly discussed. The attention is next shifted toward the automated discretization o, info:eu-repo/semantics/published

Solid oxide cells can deliver highly efficient energy conversions between electricity and fuels/chemicals. A central challenge of upscaling solid oxide cells is the probability of failure of the brittle ceramic components. The failures of the ceramic components may lead to significant degradation or eventual failure of a stack. To predict mechanical failures in a stack, a full stack model is needed, together with a local assessment of stresses at the vicinity of failing regions, e.g. the contact points between the cells and interconnects. A conventional three-dimensional model requires a very fine discretization of the mesh to capture stress intensities. Computational resources needed for such a model are therefore immense, and it is highly unlikely to compute at stack scale, as well describe the evolution over time. In this work, the homogenization modelling framework for solid oxide cell stacks is extended to identify local mechanical failures. Thus, the fracturing within a local failing point is examined by using a localization approach, where stresses in the stack model are linked to the local stresses and the energy release rate at the crack tip of the relevant interface. This is done in a general manner, such that the local stresses and the energy release rate can be evaluated at every point in the stack at every instant of time without loss of computational efficiency. A 100-cell stack can be modelled in three dimensions with all coupled multiphysics in steady state within 3 minutes on a current workstation computer

Quality and safety in the food industry, as well as the processes involved in the production, packaging, and transport of food, may be optimised using predictive microbiology. This discipline models the behaviour of microorganisms and uses the socalled primary models to describe bacterial growth. One of the main challenges consists of explaining and simulating the growth when the number of initial bacteria (inoculum) is reduced because of the stochastic behaviour. There exist different alternatives to modelling such stochastic processes depending on: (1) the required level of detail for the solution; (2) the computational time; and (3) the possibility of estimating parameters from fast and inexpensive measurements. In this work, we compare the solutions, computational times and structural identifiability obtained using different alternatives: (1) microscopic scale considering individual-based models; and (2) mesoscopic scale considering the modified Fokker-Planck equation

Напредак савремених технологија захтева фундаментално разумевање везе између структуре и својстава нових материјала. Методе прорачуна и симулације показале су се као моћно средство у испитивању ових веза, омогућавајући анализу система различитих нивоа сложености, на различитим просторним и временским скалама, са жељеним саставом и без икаквог ризика од контаминације. Штавише, константно повећање рачунарских ресурса омогућило је примену неких од најнапреднијих метода прорачуна на скали која превазилази суб-нанометарску (мали молекули и кластери атома), која се обично сматра границом за ab initio прорачуне. Иако се тачност таквих израчунавања приближава грешкама експеримента, овај приступ такође омогућава (теоријску) претрагу великог броја система, што није експериментално изводљиво, као и разумевање општих трендова који су од великог значаја, како за теоретичаре тако и за експериментаторе. Овo поглавље приказује искуства аутора у моделирању и симулацијама система нанометарских димензија различите сложености, укључујући оксиде метала, метале, угљеничне материјале, молекулске мреже и сложене каталитичке системе., Advancements in contemporary technologies require deep understanding of the link between the structure and properties of novel materials. Computational methods have been proven as a powerful tool in that quest, allowing investigation of systems of various complexity and at different spatial and temporal scales, with a desired composition and without any risk of contamination. Moreover, constant increase of computational power allowed the application of some of the most advanced computational methods at the scales which overcome sub-nanometers (small molecules and clusters of atoms), usually considered as a limit for first principles calculations. While the accuracy of such calculations reaches the experimental one, this approach also allows for the screening of a large number of systems, which is not experimentally feasible, and also the understanding of general trends which is of great importance for both theoreticians and experimentalists. This text will cover author’s recent experiences in modelling and simulation of nanoscale systems of different complexity, including metal oxides, metals, carbon materials, molecular networks and complex catalytic systems.

Multiscale models for simulating transport in porous media generally involve either upscaling techniques such as computational homogenisation, or the use of a distributed microstructure model. In the former, calculations on a representative 'mirocell' are used to derive effective coefficients for use in the macroscopic averaged equations, while in the latter coupled continuum equations are solved simultaneously at the macro and micro scales. In this talk, an overview will be provided of the computational techniques employed to solve the nonlinear conservation laws at the macroscale and the numerical methods used for simulating the potentially anomalous transport phenomena evident at the microscale. It is thought that these phenomena are driven by the constrained interactions arising within the complex and non-homogeneous microstructures evident at the pore scale. We will investigate the use of generalised transport equations based on fractional operators to model such phenomena because they have the unique ability to describe memory and non-local behaviour. Results are exhibited for the drying of lignocelluosic materials such as wood.

In this thesis we study a parameter identification problem for a stationary diffusion equation posed in heterogeneous media. This problem is closely related to the Calderón problem with anisotropic conductivities. The anisotropic case is particularly difficult and is ill-posed both in regards to uniqueness of solution and stability on the data. Since the present problem is posed in heterogeneous media, we can take advantage of multiscale modelling and the tools of homogenization theory in the study of the inverse problem, unlike the original Calderón problem. We investigate the possibilities of combining the theory of the Calderón problem with homogenization theory in order to obtain a well-posed parameter identification. We find that homogenization theory indeed can be used to make progress towards a well-posed identification of the diffusion coefficient. The success of the method is, however, dependent both on the precise structure of the heterogeneous media and on the modelling of the measurements in the invese problem framework. We have in mind a particular problem formulation which is motivated by an experiment to determine effective coefficients of materials used in food packaging. This experiment comes with a set of requirements on both the heterogeneous media and on the method for making measurements that, unfortunately, are in conflict with the currently available results for well-posedness. We study also an optimization approach to solving the inverse problem under these application specific requirements. Some progress towards well-posedness of the optimization problem is made by proving existence of minimizer, again with homogenization theory playing a key role in obtaining the result. In a proof-of-concept computational study this optimization approach is implemented and compared to two other optimization problems. For the two tested heterogeneous media, the only optimization method that manages to identify reasonably well the diffusion coefficient is th

We propose an upscaled methodology for evaluating the compressive parameters of the Lattice Discrete Particle Model (LDPM) for a multiscale analysis of concrete structures. This methodology is based on mechanical and chemical models on a wide range of concrete scales. We show that the compressive mechanical parameters are related mainly to material compaction that occurs at the scale of cement paste, the scale containing porosity properties. Therefore, these compressive LDPM parameters are subsequently evaluated based on chemical and mechanical simulations at the cement paste level. Finally, the suggested methodology was verified and validated., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Microlab

The epidermis is the outermost layer of the skin, providing a protective barrier for our bodies. Two important aspects to the barrier function of the epidermis are maintenance of its barrier layer and constant cell turnover. The main barrier layer in the epidermis is the outermost layer, called the stratum corneum. This layer blocks both the entry of antigens and the loss of internal water and solutes. If antigens do enter the system, cell turnover has been hypothesised to propel them out the system by providing a constant upwards velocity of cells which carry the toxins with them. The majority of severe diseases of the epidermis relate to a reduction in thickness of the stratum corneum. Decreased thickness reduces the barrier function of the layer, causing discomfort and inflammation. Due to its importance to barrier function, the maintenance of stratum corneum thickness, and consequently overall tissue thickness, is the focus of this thesis. In order to maintain both stratum corneum thickness and overall tissue thickness it is necessary for the system to balance cell proliferation and cell loss. Cell loss in the epidermis occurs when dead cells at the top of the tissue are lost to the environment through a process called desquamation. Cell proliferation occurs in the base, or basal, layer. As the basal cells proliferate, cells above them are pushed upwards through the tissue, causing constant upwards movement in the tissue. Not only does this contribute directly to the barrier function through the cell turnover as discussed above, but the velocity of the cells is likely to be key in regulating the tissue thickness. Assuming the cell loss occurs at a fairly constant rate, the combination of the velocity and the loss rate determine tissue thickness. In order to investigate these processes we develop a three dimensional discrete, multiscale, multicellular model, focussing on maintenance of cell proliferation and desquamation. Using this model, we are able to investig

We present the macroscale three-dimensional numerical solution of anisotropic Biot's poroelasticity, with coefficients derived from a micromechanical analysis as prescribed by the asymptotic homogenisation technique. The system of partial differential equations (PDEs) is discretised by finite elements, exploiting a formal analogy with the fully coupled thermal displacement systems of PDEs implemented in the commercial software Abaqus. The robustness of our computational framework is confirmed by comparison with the well-known analytical solution of the one-dimensional Therzaghi's consolidation problem. We then perform three-dimensional numerical simulations of the model in a sphere (representing a biological tissue) by applying a given constant pressure in the cavity. We investigate how the macroscale radial displacements (as well as pressures) profiles are affected by the microscale solid matrix compressibility (MSMC). Our results suggest that the role of the MSMC on the macroscale displacements becomes more and more prominent by increasing the length of the time interval during which the constant pressure is applied. As such, we suggest that parameter estimation based on techniques such as poroelastography (which are commonly used in the context of biological tissues, such as the brain, as well as solid tumours) should allow for a sufficiently long time in order to give a more accurate estimation of the mechanical properties of tissues.

Las enfermedades cardiovasculares tienen en la actualidad un gran impacto social y económico y constituyen una de las principales causas de mortalidad y morbilidad. Los modelos computacionales personalizados del corazón están demostrando ser útiles tanto para ayudar a comprender los mecanismos subyacentes a las patologías cardiacas como para optimizar su tratamiento prediciendo la respuesta del paciente. En este contexto, se ha puesto en marcha la Red Española de Investigación en Modelización Computacional Cardiaca (V-Heart SN). El objetivo general de V-Heart SN es el desarrollo de un modelo computacional multifísico y multiescala integrado del corazón. Este objetivo general se aborda a través de los siguientes objetivos específicos: a) integrar los diferentes modelos numéricos teniendo en cuenta la especificidad de los pacientes; b) ayudar a avanzar en el conocimiento de los mecanismos asociados a las diferentes patologías cardiacas y vasculares; y c) apoyar la aplicación de terapias personalizadas. Este artículo presenta el estado actual de la modelización computacional cardiaca y los diferentes trabajos científicos desarrollados por los miembros de la red para favorecer una mayor comprensión de las características y utilidad de los modelos., Postprint (author's final draft)

We present a methodology to quantify the essential interactions at the interface between inorganic solid nanoparticles (NPs) and biological molecules. Our model is based on pre-calculation of the repetitive contributions to the interaction from molecular segments, which allows us to efficiently scan a multitude of molecules and rank them by their adsorption affinity. The interaction between the biomolecular fragments and the nanomaterial are evaluated using a systematic coarse-graining scheme starting from all-atom molecular dynamics simulations. The NPs are modelled using a two-layer representation, where the outer layer is parameterized at the atomistic level and the core is treated at the continuum level using Lifshitz theory of dispersion forces. We demonstrate that the scheme reproduces the experimentally observed features of the NP protein coronas. To illustrate the use of the methodology, we compute the adsorption energies for human blood plasma proteins on gold NPs of different sizes as well as the preferred orientation of the molecules upon adsorption. The computed energies can be used for predicting the composition of the NP-protein corona for the corresponding material.

The innate immune system provides a first line of defense against foreign microorganisms, and is typified by the Toll-like receptor (TLR) family. TLR4 is of particular interest, since over-stimulation of its pathway by excess lipopolysaccharide (LPS) molecules from the outer membranes of Gram-negative bacteria can result in sepsis, which causes millions of deaths each year. In this review, we outline our use of molecular simulation approaches to gain a better understanding of the determinants of LPS recognition, towards the search for novel immunotherapeutics. We first describe how atomic-resolution simulations have enabled us to elucidate the regulatory conformational changes in TLR4 associated with different LPS analogues, and hence a means to rationalize experimental structure-activity data. Furthermore, multiscale modelling strategies have provided a detailed description of the thermodynamics and intermediate structures associated with the entire TLR4 relay which consists of a number of transient receptor/coreceptor complexes allowing us trace the pathway of LPS transfer from bacterial membranes to the terminal receptor complex at the plasma membrane surface. Finally, we describe our efforts to leverage these computational models, in order to elucidate previously undisclosed anti-inflammatory mechanisms of endogenous host-defense peptides found in wounds. Collectively, this work represents a promising avenue for the development of novel anti-septic treatments, inspired by nature's innate defense strategies.

Coarse graining of (geophysical) flow problems is a necessity brought upon us by the wide range of spatial and temporal scales present in these problems, which cannot be all represented on a numerical grid without an inordinate amount of computational resources. Traditionally, the effect of the unresolved eddies is approximated by deterministic closure models, i.e. so-called parameterizations. The effect of the unresolved eddy field enters the resolved-scale equations as a forcing term, denoted as the’eddy forcing’. Instead of creating a deterministic parameterization, our goal is to infer a stochastic, data-driven surrogate model for the eddy forcing from a (limited) set of reference data, with the goal of accurately capturing the long-term flow statistics. Our surrogate modelling approach essentially builds on a resampling strategy, where we create a probability density function of the reference data that is conditional on (time-lagged) resolved-scale variables. The choice of resolved-scale variables, as well as the employed time lag, is essential to the performance of the surrogate. We will demonstrate the effect of different modelling choices on a simplified ocean model of two-dimensional turbulence in a doubly periodic square domain.

Particle based approaches are one of the recent modeling techniques to overcome the computational limitation in multiscale modeling of complex processes, for example a heterogeneous catalytic reactor. We propose an efficient model for a chemical reactor where hydrodynamics of the solvent is determined by Stochastic Rotation Dynamics and a reaction occurs over a catalytic surface where the reaction kinetics follows the mean-field assumption. We highlight the modeling techniques required to simulate such a system and then validate the model for its separate and combined components of convection, diffusion and reaction(s). A dimensionless analysis helps compare processes occurring at different scales. We determine the Reynolds number, Re, and the Damkohler numbers, Da and DaL in terms of key quantities. The approach is then used to analyse a reaction (a) following the Langmuir-Hinshelwood kinetics, (b) generating product particles with different self-diffusivity values as compared to the reactant particles. The model developed can further incorporate reactions occurring inside complex geometries (pore diffusion) and also be used to study complex reaction systems for which the mean-field assumption is no longer valid., Complex Fluid Processing, Intensified Reaction and Separation Systems

This research project is a step forward in predicting the bulk-scale deformations in food-plant materials during drying in a computationally efficient and accurate manner. In the proposed numerical modelling method, a novel force interaction field was introduced to represent the mechanics of a whole plant cell during drying through its centroid. By utilising this novel modelling approach, the distinct deformation profiles of several food-plant materials were revealed along with the impact of drying situations and features in the microstructure. The developed modelling framework has potential applications in the field of food engineering in controlling quality attributes of dried food-plant products.

Particle based approaches are one of the recent modeling techniques to overcome the computational limitation in multiscale modeling of complex processes, for example a heterogeneous catalytic reactor. We propose an efficient model for a chemical reactor where hydrodynamics of the solvent is determined by Stochastic Rotation Dynamics and a reaction occurs over a catalytic surface where the reaction kinetics follows the mean-field assumption. We highlight the modeling techniques required to simulate such a system and then validate the model for its separate and combined components of convection, diffusion and reaction(s). A dimensionless analysis helps compare processes occurring at different scales. We determine the Reynolds number, Re, and the Damkohler numbers, Da and DaL in terms of key quantities. The approach is then used to analyse a reaction (a) following the Langmuir-Hinshelwood kinetics, (b) generating product particles with different self-diffusivity values as compared to the reactant particles. The model developed can further incorporate reactions occurring inside complex geometries (pore diffusion) and also be used to study complex reaction systems for which the mean-field assumption is no longer valid.

Numerical modelling has emerged as a powerful and effective tool to study various dynamic behaviours of biological matter. Such numerical modelling tools have contributed to the optimisations of food drying parameters leading to higher quality end-products in the field of food engineering. In this context, one of the most recent developments is the meshfree-based numerical models, which have demonstrated enhanced capabilities to model cellular deformations during drying, providing many benefits compared to conventional grid-based modelling approaches. However, the potential extension of this method for simulating bulk level tissues has been a challenge due to the increased requirement for higher computational time and resources. As a solution for this, by incorporating meshfree features, a novel coarse-grained multiscale numerical model is proposed in this work to predict bulk level (macroscale) deformations of food-plant tissues during drying. Accordingly, realistic simulation of morphological changes of apple tissues can now be performed with just 2% of the previous computational time in microscale and macroscale simulations can also be conducted. Compared to contemporary multiscale models, this modelling approach provides more convenient computational implementation as well. Thus, this novel approach can be recommended for efficiently and accurately simulating morphological changes of cellular materials undergoing drying processes, while confirming its potential future expansion to efficiently and accurately predict morphological changes of heterogeneous plant tissues in different spatial scales.

This paper presents a new polymorphic element modelling approach for multi-scale simulation, with an application to fracture in composite structures. We propose the concept of polymorphic elements; these are elements that exist as an evolving superposition of various states, each representing the relevant physics with the required level of fidelity. During a numerical simulation, polymorphic elements can change their formulation to more effectively represent the structural state or to improve computational efficiency. This change is achieved by transitioning progressively between states and by repartitioning each state on-the-fly as required at any given instant during the analysis. In this way, polymorphic elements offer the possibility to carry out a multiscale simulation without having to define a priori where the local model should be located. Polymorphic elements can be implemented as simple user-defined elements which can be readily integrated in a Finite Element code. Each individual user-defined polymorphic element contains all the relevant superposed states (and their coupling), as well as the ability to self-refine. We implemented a polymorphic element with continuum (plain strain) and structural (beam) states for the multiscale simulation of crack propagation. To verify the formulation, we applied it to the multiscale simulation of known mode I, mode II andmixed-mode I and II crack propagation scenarios, obtaining good accuracy and up to 70% reduction in computational time —the reduction in computational time can potentially be even more significant for large engineering structures where the local model is a small portion of the total. We further applied our polymorphic element formulation to the multiscale simulation of a more complex problem involving interaction between cracks (delamination migration), thereby demonstrating the potential impact of the proposed multiscale modelling approach for realistic engineering problems., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Aerospace Structures & Computational Mechanics

The so-called Locally Resonant Acoustic Metamaterials (LRAM) are considered for the design of specifically engineered devices capable of stopping waves from propagating in certain frequency regions (bandgaps), this making them applicable for acoustic insulation purposes. This fact has inspired the design of a new kind of lightweight acoustic insulation panels with the ability to attenuate noise sources in the low frequency range (below 5000 Hz) without requiring thick pieces of very dense materials. A design procedure based on different computational mechanics tools, namely, (1) a multiscale homogenization framework, (2) model order reduction strategies and (3) topological optimization procedures, is proposed. It aims at attenuating sound waves through the panel for a target set of resonance frequencies as well as maximizing the associated bandgaps. The resulting design’s performance is later studied by introducing viscoelastic properties in the coating phase, in order to both analyse their effects on the overall design and account for more realistic behaviour. The study displays the emerging field of Computational Material Design (CMD) as a computational mechanics area with enormous potential for the design of metamaterial-based industrial acoustic parts., Peer Reviewed, Postprint (author's final draft)